Study on Spatial Model and Service Radius of Rural Areas and Agriculture Information Level in Yellow-river Delta Yujian Yang1, Guangming Liu2, Xueqin Tong1, Zhicheng Wang1 1.S & T Information Engineering Technology Center of Shandong Academy of Agricultural Science, Information center of agronomy College of Shandong University Jinan 250100, P. R. China 2. Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, P. R. China 1 Corresponding author, Address: S&T Information Engineering Research Center, Number 202 Gongye North Road, Licheng District of Jinan, 250100, Shandong Province, P. R. China, Tel: +86-531-83179076, Fax:+86-531-83179821, Email:[email protected] Abstract. Based on the evaluation methods and systems of information measurement level, and according to the principles of agriculture information subject, the study constructed 13 indices system for the measurement of the rural areas and agriculture information level in Yellow-river Delta in 2007. Spatial autocorrelation model of rural areas and agriculture information of 19 country units showed that the comprehensive information level of Hanting, Shouguang, Guangrao, Bincheng, Huimin, Wudi and Yangxin country unit is very significant, has the obvious spatial agglomeration and homogeneity characteristics, but information level agglomeration of Kenli country and Zouping city has the significant heterogeneity, and information level agglomeration characteristics of other 10 country units is not significant. The radius surface of the complicated information level from radial basis function model indicated that rural areas and agriculture information service has a certain service radius, the distance of service radius in theory is 30Km, the gradient and hierarchy is obvious. According to it, the comprehensive service node should be established in Bincheng district and the secondary service node should be set up in Wudi country for improving the service efficiency. Combining with GIS grid technology, the profile line results of rural areas and agriculture information service from centroid coordinates based on 19 country units illustrated the fluctuation characteristics of the comprehensive information level. For yellow-river Delta, the high-efficiency ecological zone construction should consider the gradients of information service, continuity of information service and spatial agglomeration characteristics of 19 country units. Correspondingly, the regional policy which reflected the regional difference and association characteristics was carried out to achieve the leap development. Keywords: Rural areas and agriculture information level; Service Radius; Measurement indices; Radial basis function; Yellow-river Delta 1 Introduction Rural areas and agriculture Information is not only the inevitable trend of the modern agricultural development, but also an important component of the national economy information (Zang Chunrong et al., 2004). Rural areas and agriculture information assessment is to evaluate the level and effect of rural information construction and development level by adopting mathematical statistics(such as AHP method, fuzzy theory study), operation research principle and specific indicator system (Liu Shihong et al., 2007; Du Jing et al., 2010). Evaluating rural information level comprehensively, measuring and grasping the information development level accurately will help to find problems and shortcomings in the process of rural information. Rural information assessment is a huge system engineering that synthesizes technological and institutional innovations. Its implementation involves all aspects of rural business and information technology (Qin Xiangyang et al., 2008)[1,2,3,4,5,6,7]. Rural areas and agriculture information constructions reflected the science and technology progress and the main promotion power of agriculture economy(Chen Xiwen, 2010), the present study showed that the correlation domains involved in the evaluation indices system of rural areas and agriculture information, but the measurement evaluation of it should be strengthened on the regional scale to intensify the agriculture resources and high-efficiency utilization of rural areas and agriculture information. The regional measurement of rural areas and agriculture information expanded the open degree and propelled the regional economy development, the service scope and service radius study of information level is rarely on the regional scale from the correlation documents[8]. There is crucial to select the service node of rural areas and agriculture information level, whether in theory, or in practice for the comprehensive service system of information level. The study selected the regional economy body—Yellow river delta, as demonstration site, and explored the regional effects of rural areas and agriculture information level. Furthermore, the gradient and hierarchy of rural areas and agriculture information service was developed in the study, with the support of spatial analysis technology, the important service node of rural area and agriculture information level where the suitable location is selected was analyzed to improve the service efficiency in Yellow river delta. And other emphasis study is the fluctuation characteristics of the comprehensive information level of 19 country units in the paper. 2 Material and Methodology 2.1 Study Area and Indices System Establishment Yellow river delta was formed on the basis of the yellow river alluvial plain and the adjacent marine regions in the north parts of Shandong province, extension the round regions, including Dongying city, Binzhou city, Hanting, Shouguang city, Changyi city in Weifang city, Leling and Qingyun in Dezhou city, Gaoqing in Zibo city and Laizhou in Yantai city. The total area is 26500Km2, the 1/6 of the total province. Yellow river Delta is the high-efficiency ecological zone, which has the important strategic significance, it located in the north emphasis of the whole strategy in Shandong province and is the important part of ―blue economic zone of peninsula‖, the comprehensive development of rural areas and agriculture information level in Shandong province is crucial part of the national demonstration province of rural areas and agriculture information on 11th and 12th five year development planning of China. Indices system establishment of rural areas and agriculture information referred to the new countryside planning of socialism construction from development and reform commission of Shandong province "developed production, affluent life, civilized rural atmosphere, clean and tidy village and democratic administration." Combing with well-off indices from Wu Dianting (2006), and considering the measurement system rule, study data were effectively collected and the selected 13 indices was applied in the study[9].The 6 indices reflected material life (Enger coefficient, per house area of household, tap water countryside, household car, hospital numbers, urbanization). The 3 indices reflected spiritual life (telephone communication countryside, cable television countryside, patents per year). The income and distribution situation, included 2 indices（per capita net income and per capita GDP. Population quality considered 2 indices (students in middle school and per capita expenditure of education, science-technology and culture). In circumstances, Data acquisition, elimination dimension, the determination of indices weight, model establishment, the comprehensive indices computerization and comparison analysis, a series of steps were carried out to deal with the correlation data. As mentioned, measurement indices system was used to explore the autocorrelation characteristics and service radius of rural areas and agriculture information level[10,11]. 2.2 Moran’s I Coefficient and Radial Basis Function Spatial autocorrelation can be defined as the coincidence of value similarity with site similarity. As the name suggests, autocorrelation is the correlation of a variable with itself, the autocorrelation coefficient (Moran’s I) was used as description about the spatial dependency and was selected to describe the correlation degree of the location attribution value. The Moran’s I coefficient is given n(xi x) by: I 2 (1) W (xi x) Where xi is the data value at location i , h is the distance between locations and j , wij takes 1 if the pair (,)ij pertains to distance class (the one for which the coefficient is computed), otherwise 0, W is the sum of . Where: I [1,1], I 0 stands for the independent variables, I 0 stands for the positive correlation, I 0 stands for the negative correlation[12,13,14]. Moran’s I coefficient reflecting spatial dependency embodied the similarity of nearby observations. If high values of an attribute tend to cluster together in some parts of country unit and low values tend to cluster together in other parts, the attribute is said to exhibit positive spatial autocorrelation. Conversely, if high values tend to be found in close proximity to low values and vice versa, the attribute is said to exhibit negative spatial autocorrelation[15]. Radial basis function (RBF) enables you to create a surface that captures global trends and picks up the local variation, which helps in cases where fitting a plane to the sample values will not accurately represent the surface. To create the surface, suppose you have the ability to bend and stretch the predicted surface so that it passes through all of the measured values. The study uses a set of n basis functions, one for each data